chore: initial snapshot for gitea/github upload
This commit is contained in:
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"""Generic prompt management integration for LiteLLM."""
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from typing import TYPE_CHECKING, Optional
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if TYPE_CHECKING:
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from .generic_prompt_manager import GenericPromptManager
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from litellm.types.prompts.init_prompts import PromptLiteLLMParams, PromptSpec
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from litellm.integrations.custom_prompt_management import CustomPromptManagement
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from litellm.types.prompts.init_prompts import SupportedPromptIntegrations
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from .generic_prompt_manager import GenericPromptManager
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# Global instances
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global_generic_prompt_config: Optional[dict] = None
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def set_global_generic_prompt_config(config: dict) -> None:
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"""
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Set the global generic prompt configuration.
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Args:
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config: Dictionary containing generic prompt configuration
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- api_base: Base URL for the API
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- api_key: Optional API key for authentication
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- timeout: Request timeout in seconds (default: 30)
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"""
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import litellm
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litellm.global_generic_prompt_config = config # type: ignore
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def prompt_initializer(
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litellm_params: "PromptLiteLLMParams", prompt_spec: "PromptSpec"
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) -> "CustomPromptManagement":
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"""
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Initialize a prompt from a generic prompt management API.
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"""
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prompt_id = getattr(litellm_params, "prompt_id", None)
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api_base = litellm_params.api_base
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api_key = litellm_params.api_key
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if not api_base:
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raise ValueError("api_base is required in generic_prompt_config")
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provider_specific_query_params = litellm_params.provider_specific_query_params
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try:
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generic_prompt_manager = GenericPromptManager(
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api_base=api_base,
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api_key=api_key,
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prompt_id=prompt_id,
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additional_provider_specific_query_params=provider_specific_query_params,
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**litellm_params.model_dump(
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exclude_none=True,
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exclude={
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"prompt_id",
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"api_key",
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"provider_specific_query_params",
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"api_base",
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},
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),
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)
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return generic_prompt_manager
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except Exception as e:
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raise e
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prompt_initializer_registry = {
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SupportedPromptIntegrations.GENERIC_PROMPT_MANAGEMENT.value: prompt_initializer,
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}
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# Export public API
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__all__ = [
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"GenericPromptManager",
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"set_global_generic_prompt_config",
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"global_generic_prompt_config",
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"prompt_initializer_registry",
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]
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@@ -0,0 +1,499 @@
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"""
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Generic prompt manager that integrates with LiteLLM's prompt management system.
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Fetches prompts from any API that implements the /beta/litellm_prompt_management endpoint.
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"""
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import json
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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import httpx
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from litellm.integrations.custom_prompt_management import CustomPromptManagement
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from litellm.integrations.prompt_management_base import (
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PromptManagementBase,
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PromptManagementClient,
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)
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from litellm.llms.custom_httpx.http_handler import (
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_get_httpx_client,
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get_async_httpx_client,
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)
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from litellm.types.llms.custom_http import httpxSpecialProvider
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from litellm.types.llms.openai import AllMessageValues
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from litellm.types.prompts.init_prompts import PromptSpec
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from litellm.types.utils import StandardCallbackDynamicParams
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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class GenericPromptManager(CustomPromptManagement):
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"""
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Generic prompt manager that integrates with LiteLLM's prompt management system.
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This class enables using prompts from any API that implements the
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/beta/litellm_prompt_management endpoint.
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Usage:
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# Configure API access
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generic_config = {
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"api_base": "https://your-api.com",
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"api_key": "your-api-key", # optional
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"timeout": 30, # optional, defaults to 30
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}
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# Use with completion
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response = litellm.completion(
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model="generic_prompt/gpt-4",
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prompt_id="my_prompt_id",
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prompt_variables={"variable": "value"},
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generic_prompt_config=generic_config,
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messages=[{"role": "user", "content": "Additional message"}]
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)
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"""
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def __init__(
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self,
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api_base: str,
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api_key: Optional[str] = None,
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timeout: int = 30,
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prompt_id: Optional[str] = None,
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additional_provider_specific_query_params: Optional[Dict[str, Any]] = None,
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**kwargs,
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):
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"""
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Initialize the Generic Prompt Manager.
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Args:
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api_base: Base URL for the API (e.g., "https://your-api.com")
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api_key: Optional API key for authentication
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timeout: Request timeout in seconds (default: 30)
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prompt_id: Optional prompt ID to pre-load
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"""
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super().__init__(**kwargs)
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self.api_base = api_base.rstrip("/")
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self.api_key = api_key
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self.timeout = timeout
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self.prompt_id = prompt_id
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self.additional_provider_specific_query_params = (
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additional_provider_specific_query_params
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)
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self._prompt_cache: Dict[str, PromptManagementClient] = {}
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@property
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def integration_name(self) -> str:
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"""Integration name used in model names like 'generic_prompt/gpt-4'."""
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return "generic_prompt"
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def _get_headers(self) -> Dict[str, str]:
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"""Get HTTP headers for API requests."""
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headers = {
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"Content-Type": "application/json",
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"Accept": "application/json",
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}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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return headers
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def _fetch_prompt_from_api(
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self, prompt_id: Optional[str], prompt_spec: Optional[PromptSpec]
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) -> Dict[str, Any]:
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"""
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Fetch a prompt from the API.
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Args:
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prompt_id: The ID of the prompt to fetch
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Returns:
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The prompt data from the API
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Raises:
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Exception: If the API request fails
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"""
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if prompt_id is None and prompt_spec is None:
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raise ValueError("prompt_id or prompt_spec is required")
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url = f"{self.api_base}/beta/litellm_prompt_management"
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params = {
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"prompt_id": prompt_id,
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**(self.additional_provider_specific_query_params or {}),
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}
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http_client = _get_httpx_client()
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try:
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response = http_client.get(
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url,
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params=params,
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headers=self._get_headers(),
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)
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response.raise_for_status()
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return response.json()
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except httpx.HTTPError as e:
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raise Exception(f"Failed to fetch prompt '{prompt_id}' from API: {e}")
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except json.JSONDecodeError as e:
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raise Exception(f"Failed to parse prompt response for '{prompt_id}': {e}")
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async def async_fetch_prompt_from_api(
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self, prompt_id: Optional[str], prompt_spec: Optional[PromptSpec]
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) -> Dict[str, Any]:
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"""
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Fetch a prompt from the API asynchronously.
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"""
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if prompt_id is None and prompt_spec is None:
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raise ValueError("prompt_id or prompt_spec is required")
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url = f"{self.api_base}/beta/litellm_prompt_management"
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params = {
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"prompt_id": prompt_id,
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**(
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prompt_spec.litellm_params.provider_specific_query_params
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if prompt_spec
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and prompt_spec.litellm_params.provider_specific_query_params
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else {}
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),
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}
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http_client = get_async_httpx_client(
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llm_provider=httpxSpecialProvider.PromptManagement,
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)
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try:
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response = await http_client.get(
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url,
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params=params,
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headers=self._get_headers(),
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)
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response.raise_for_status()
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return response.json()
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except httpx.HTTPError as e:
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raise Exception(f"Failed to fetch prompt '{prompt_id}' from API: {e}")
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except json.JSONDecodeError as e:
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raise Exception(f"Failed to parse prompt response for '{prompt_id}': {e}")
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def _parse_api_response(
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self,
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prompt_id: Optional[str],
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prompt_spec: Optional[PromptSpec],
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api_response: Dict[str, Any],
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) -> PromptManagementClient:
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"""
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Parse the API response into a PromptManagementClient structure.
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Expected API response format:
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{
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"prompt_id": "string",
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"prompt_template": [
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{"role": "system", "content": "..."},
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{"role": "user", "content": "..."}
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],
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"prompt_template_model": "gpt-4", # optional
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"prompt_template_optional_params": { # optional
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"temperature": 0.7,
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"max_tokens": 100
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}
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}
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Args:
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prompt_id: The ID of the prompt
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api_response: The response from the API
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Returns:
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PromptManagementClient structure
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"""
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return PromptManagementClient(
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prompt_id=prompt_id,
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prompt_template=api_response.get("prompt_template", []),
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prompt_template_model=api_response.get("prompt_template_model"),
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prompt_template_optional_params=api_response.get(
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"prompt_template_optional_params"
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),
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completed_messages=None,
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)
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def should_run_prompt_management(
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self,
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prompt_id: Optional[str],
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prompt_spec: Optional[PromptSpec],
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dynamic_callback_params: StandardCallbackDynamicParams,
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) -> bool:
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"""
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Determine if prompt management should run based on the prompt_id.
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For Generic Prompt Manager, we always return True and handle the prompt loading
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in the _compile_prompt_helper method.
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"""
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if prompt_id is not None or (
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prompt_spec is not None
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and prompt_spec.litellm_params.provider_specific_query_params is not None
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):
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return True
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return False
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def _get_cache_key(
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self,
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prompt_id: Optional[str],
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prompt_label: Optional[str] = None,
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prompt_version: Optional[int] = None,
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) -> str:
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return f"{prompt_id}:{prompt_label}:{prompt_version}"
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def _common_caching_logic(
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self,
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prompt_id: Optional[str],
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prompt_label: Optional[str] = None,
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prompt_version: Optional[int] = None,
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prompt_variables: Optional[dict] = None,
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) -> Optional[PromptManagementClient]:
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"""
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Common caching logic for the prompt manager.
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"""
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# Check cache first
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cache_key = self._get_cache_key(prompt_id, prompt_label, prompt_version)
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if cache_key in self._prompt_cache:
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cached_prompt = self._prompt_cache[cache_key]
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# Return a copy with variables applied if needed
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if prompt_variables:
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return self._apply_variables(cached_prompt, prompt_variables)
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return cached_prompt
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return None
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def _compile_prompt_helper(
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self,
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prompt_id: Optional[str],
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prompt_spec: Optional[PromptSpec],
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prompt_variables: Optional[dict],
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dynamic_callback_params: StandardCallbackDynamicParams,
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prompt_label: Optional[str] = None,
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prompt_version: Optional[int] = None,
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) -> PromptManagementClient:
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"""
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Compile a prompt template into a PromptManagementClient structure.
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This method:
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1. Fetches the prompt from the API (with caching)
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2. Applies any prompt variables (if the API supports it)
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3. Returns the structured prompt data
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Args:
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prompt_id: The ID of the prompt
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prompt_variables: Variables to substitute in the template (optional)
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dynamic_callback_params: Dynamic callback parameters
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prompt_label: Optional label for the prompt version
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prompt_version: Optional specific version number
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Returns:
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PromptManagementClient structure
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"""
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cached_prompt = self._common_caching_logic(
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prompt_id=prompt_id,
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prompt_label=prompt_label,
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prompt_version=prompt_version,
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prompt_variables=prompt_variables,
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)
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if cached_prompt:
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return cached_prompt
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cache_key = self._get_cache_key(prompt_id, prompt_label, prompt_version)
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try:
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# Fetch from API
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api_response = self._fetch_prompt_from_api(prompt_id, prompt_spec)
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# Parse the response
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prompt_client = self._parse_api_response(
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prompt_id, prompt_spec, api_response
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)
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# Cache the result
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self._prompt_cache[cache_key] = prompt_client
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# Apply variables if provided
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if prompt_variables:
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prompt_client = self._apply_variables(prompt_client, prompt_variables)
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return prompt_client
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except Exception as e:
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raise ValueError(f"Error compiling prompt '{prompt_id}': {e}")
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async def async_compile_prompt_helper(
|
||||
self,
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||||
prompt_id: Optional[str],
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||||
prompt_variables: Optional[dict],
|
||||
dynamic_callback_params: StandardCallbackDynamicParams,
|
||||
prompt_spec: Optional[PromptSpec] = None,
|
||||
prompt_label: Optional[str] = None,
|
||||
prompt_version: Optional[int] = None,
|
||||
) -> PromptManagementClient:
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||||
# Check cache first
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||||
cached_prompt = self._common_caching_logic(
|
||||
prompt_id=prompt_id,
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||||
prompt_label=prompt_label,
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||||
prompt_version=prompt_version,
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||||
prompt_variables=prompt_variables,
|
||||
)
|
||||
if cached_prompt:
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||||
return cached_prompt
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||||
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||||
cache_key = self._get_cache_key(prompt_id, prompt_label, prompt_version)
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||||
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try:
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||||
# Fetch from API
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||||
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||||
api_response = await self.async_fetch_prompt_from_api(
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||||
prompt_id=prompt_id, prompt_spec=prompt_spec
|
||||
)
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||||
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||||
# Parse the response
|
||||
prompt_client = self._parse_api_response(
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||||
prompt_id, prompt_spec, api_response
|
||||
)
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||||
|
||||
# Cache the result
|
||||
self._prompt_cache[cache_key] = prompt_client
|
||||
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||||
# Apply variables if provided
|
||||
if prompt_variables:
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||||
prompt_client = self._apply_variables(prompt_client, prompt_variables)
|
||||
|
||||
return prompt_client
|
||||
|
||||
except Exception as e:
|
||||
raise ValueError(
|
||||
f"Error compiling prompt '{prompt_id}': {e}, prompt_spec: {prompt_spec}"
|
||||
)
|
||||
|
||||
def _apply_variables(
|
||||
self,
|
||||
prompt_client: PromptManagementClient,
|
||||
variables: Dict[str, Any],
|
||||
) -> PromptManagementClient:
|
||||
"""
|
||||
Apply variables to the prompt template.
|
||||
|
||||
This performs simple string substitution using {variable_name} syntax.
|
||||
|
||||
Args:
|
||||
prompt_client: The prompt client structure
|
||||
variables: Variables to substitute
|
||||
|
||||
Returns:
|
||||
Updated PromptManagementClient with variables applied
|
||||
"""
|
||||
# Create a copy of the prompt template with variables applied
|
||||
updated_messages: List[AllMessageValues] = []
|
||||
for message in prompt_client["prompt_template"]:
|
||||
updated_message = dict(message) # type: ignore
|
||||
if "content" in updated_message and isinstance(
|
||||
updated_message["content"], str
|
||||
):
|
||||
content = updated_message["content"]
|
||||
for key, value in variables.items():
|
||||
content = content.replace(f"{{{key}}}", str(value))
|
||||
content = content.replace(
|
||||
f"{{{{{key}}}}}", str(value)
|
||||
) # Also support {{key}}
|
||||
updated_message["content"] = content
|
||||
updated_messages.append(updated_message) # type: ignore
|
||||
|
||||
return PromptManagementClient(
|
||||
prompt_id=prompt_client["prompt_id"],
|
||||
prompt_template=updated_messages,
|
||||
prompt_template_model=prompt_client["prompt_template_model"],
|
||||
prompt_template_optional_params=prompt_client[
|
||||
"prompt_template_optional_params"
|
||||
],
|
||||
completed_messages=None,
|
||||
)
|
||||
|
||||
async def async_get_chat_completion_prompt(
|
||||
self,
|
||||
model: str,
|
||||
messages: List[AllMessageValues],
|
||||
non_default_params: dict,
|
||||
prompt_id: Optional[str],
|
||||
prompt_variables: Optional[dict],
|
||||
dynamic_callback_params: StandardCallbackDynamicParams,
|
||||
litellm_logging_obj: "LiteLLMLoggingObj",
|
||||
prompt_spec: Optional[PromptSpec] = None,
|
||||
tools: Optional[List[Dict]] = None,
|
||||
prompt_label: Optional[str] = None,
|
||||
prompt_version: Optional[int] = None,
|
||||
ignore_prompt_manager_model: Optional[bool] = False,
|
||||
ignore_prompt_manager_optional_params: Optional[bool] = False,
|
||||
) -> Tuple[str, List[AllMessageValues], dict]:
|
||||
"""
|
||||
Get chat completion prompt and return processed model, messages, and parameters.
|
||||
"""
|
||||
|
||||
return await PromptManagementBase.async_get_chat_completion_prompt(
|
||||
self,
|
||||
model,
|
||||
messages,
|
||||
non_default_params,
|
||||
prompt_id=prompt_id,
|
||||
prompt_variables=prompt_variables,
|
||||
litellm_logging_obj=litellm_logging_obj,
|
||||
dynamic_callback_params=dynamic_callback_params,
|
||||
prompt_spec=prompt_spec,
|
||||
tools=tools,
|
||||
prompt_label=prompt_label,
|
||||
prompt_version=prompt_version,
|
||||
ignore_prompt_manager_model=(
|
||||
ignore_prompt_manager_model
|
||||
or prompt_spec.litellm_params.ignore_prompt_manager_model
|
||||
if prompt_spec
|
||||
else False
|
||||
),
|
||||
ignore_prompt_manager_optional_params=(
|
||||
ignore_prompt_manager_optional_params
|
||||
or prompt_spec.litellm_params.ignore_prompt_manager_optional_params
|
||||
if prompt_spec
|
||||
else False
|
||||
),
|
||||
)
|
||||
|
||||
def get_chat_completion_prompt(
|
||||
self,
|
||||
model: str,
|
||||
messages: List[AllMessageValues],
|
||||
non_default_params: dict,
|
||||
prompt_id: Optional[str],
|
||||
prompt_variables: Optional[dict],
|
||||
dynamic_callback_params: StandardCallbackDynamicParams,
|
||||
prompt_spec: Optional[PromptSpec] = None,
|
||||
prompt_label: Optional[str] = None,
|
||||
prompt_version: Optional[int] = None,
|
||||
ignore_prompt_manager_model: Optional[bool] = False,
|
||||
ignore_prompt_manager_optional_params: Optional[bool] = False,
|
||||
) -> Tuple[str, List[AllMessageValues], dict]:
|
||||
"""
|
||||
Get chat completion prompt and return processed model, messages, and parameters.
|
||||
"""
|
||||
return PromptManagementBase.get_chat_completion_prompt(
|
||||
self,
|
||||
model,
|
||||
messages,
|
||||
non_default_params,
|
||||
prompt_id=prompt_id,
|
||||
prompt_variables=prompt_variables,
|
||||
dynamic_callback_params=dynamic_callback_params,
|
||||
prompt_spec=prompt_spec,
|
||||
prompt_label=prompt_label,
|
||||
prompt_version=prompt_version,
|
||||
ignore_prompt_manager_model=(
|
||||
ignore_prompt_manager_model
|
||||
or prompt_spec.litellm_params.ignore_prompt_manager_model
|
||||
if prompt_spec
|
||||
else False
|
||||
),
|
||||
ignore_prompt_manager_optional_params=(
|
||||
ignore_prompt_manager_optional_params
|
||||
or prompt_spec.litellm_params.ignore_prompt_manager_optional_params
|
||||
if prompt_spec
|
||||
else False
|
||||
),
|
||||
)
|
||||
|
||||
def clear_cache(self) -> None:
|
||||
"""Clear the prompt cache."""
|
||||
self._prompt_cache.clear()
|
||||
Reference in New Issue
Block a user